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    施涛 – 第4页 – 从数据中看规律
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          施涛
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       <article class="full-content post-912 post type-post status-publish format-standard hentry category-84 category-83 category-66 tag-berkeley tag-leo-breiman tag-peter-bickel tag-56 tag-55 tag-50 tag-51 category-84-id category-83-id category-66-id post-seq-1 post-parity-odd meta-position-corners fix" id="post-912">
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           <a class="entry-title" href="http://blog.cos.name/taoshi/2012/02/18/leo-breiman-speech-in-chinese/" rel="bookmark" title="25年后的统计系会是什么样？(Leo Breiman在1994年伯克利统计系毕业典礼致辞)">
            25年后的统计系会是什么样？(Leo Breiman在1994年伯克利统计系毕业典礼致辞)
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            <a href="http://blog.cos.name/taoshi/category/%e7%bb%9f%e8%ae%a1/" rel="category tag">
             统计
            </a>
            ,
            <a href="http://blog.cos.name/taoshi/category/%e7%bb%9f%e8%ae%a1%e5%92%a8%e8%af%a2/" rel="category tag">
             统计咨询
            </a>
            ,
            <a href="http://blog.cos.name/taoshi/category/%e7%bb%9f%e8%ae%a1%e5%ba%94%e7%94%a8/" rel="category tag">
             统计应用
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          <span class="month">
           2月
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          <span class="day">
           18
          </span>
          <span class="year">
           2012
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          <p>
           [Note: This is a translation of the speech given by Professor Leo Breiman at the graduation ceremony of Department of Statistics at University of California， Berkeley in 1994.
           <a href="http://www.stat.berkeley.edu/%7Edpurdy/Breiman-1994-commencement.html" target="_blank">
            The original online version of the transcript
           </a>
           is credited to
           <a href="http://www.stat.berkeley.edu/~dpurdy/" target="_blank">
            David Purdy
           </a>
           and how he dug this master piece out is mentioned
           <a href="http://taoshistat.wordpress.com/2011/10/12/leos-prediction-from-1994/" target="_blank">
            here
           </a>
           .]
          </p>
          <p>
           <strong>
            伯克利统计系1994年毕业典礼致辞
           </strong>
          </p>
          <p>
           by
           <a href="http://www.stat.berkeley.edu/~breiman/" target="_blank">
            Professor Leo Breiman
           </a>
          </p>
          <p>
           很久以前，Peter Bickel 请我做一个毕业典礼致辞。Peter 是聪明的。他知道，如果你提前很久请人在似乎很遥远的将来发言，他们可能会答应。我答应了。但随着时间的到来，现实紧凑起来。朋友说，“讲一些很长的笑话”。除了我不擅长讲笑话的事实外，讲笑话似乎也不恰当。相反，我决定谈谈我们共同的东西 – 这个我们都是其中一部分的，古怪和有趣的领域。
          </p>
          <p>
           <span class="more-link fix">
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          at 14:03
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           Leo Breiman
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          ,
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           Peter Bickel
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          ,
          <a href="http://blog.cos.name/taoshi/tag/%e4%bc%af%e5%85%8b%e5%88%a9%e7%bb%9f%e8%ae%a1%e7%b3%bb/" rel="tag">
           伯克利统计系
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          ,
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           毕业典礼
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          ,
          <a href="http://blog.cos.name/taoshi/tag/%e7%b2%be%e7%ae%97%e5%b8%88/" rel="tag">
           精算师
          </a>
          ,
          <a href="http://blog.cos.name/taoshi/tag/%e7%bb%9f%e8%ae%a1%e5%ad%a6%e5%ae%b6/" rel="tag">
           统计学家
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           <a class="entry-title" href="http://blog.cos.name/taoshi/2012/02/10/predicting-feeling/" rel="bookmark" title="你情绪好吗？请问计算机">
            你情绪好吗？请问计算机
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          <p>
           好久以前我在
           <a href="http://taoshistat.wordpress.com/2011/09/21/fcwr/">
            闲扯“非诚勿扰”
           </a>
           中可能的数据挖掘时提到：
          </p>
          <blockquote>
           <p>
            根据150多期的节目录像，数据挖掘可以做什么？或许可以搭建一些黑盒子（也就是算法）。我们可以把女同学们（从男同学上场到第一次有机会灭灯之间）的表情扔进一个盒子，盒子很短时间内给出这次几盏灯会灭的预测，当然更好的盒子或许还可以预测到底哪几盏灯会灭。
           </p>
          </blockquote>
          <p>
           <a href="http://yihui.name/" target="_blank">
            有人
           </a>
           评论“人心太难测了”，还有
           <a href="http://www.cnblogs.com/zuoqs" target="_blank">
            搞数据挖掘的朋友
           </a>
           建议要分析清楚“未婚男女到底想要找什么类型的异性做理想伴侣？如何确定哪些属性是做匹配必须知道的因而是重要的？如何判断应征者的各种重要的属性？如何给应征者归类？”
          </p>
          <p>
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          at 22:10
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           情绪预测
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           数据挖掘
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          ,
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           超级碗
          </a>
          ,
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           非诚勿扰
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       <article class="full-content post-642 post type-post status-publish format-standard hentry category-84 category-66 category-3 tag-air-pollution tag-fine-particulate-matter tag-meta-analysis tag-pm2-5 tag-ralph-kahn tag-satellite tag-yang-liu tag-15 category-84-id category-66-id category-3-id post-seq-3 post-parity-odd meta-position-corners fix" id="post-642">
        <header class="post-header title-container fix">
         <div class="title">
          <h2 class="posttitle">
           <a class="entry-title" href="http://blog.cos.name/taoshi/2011/12/28/pm2-5/" rel="bookmark" title="民科一下pm2.5">
            民科一下pm2.5
           </a>
          </h2>
          <div class="postdata fix">
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            </span>
            <a href="http://blog.cos.name/taoshi/category/%e7%bb%9f%e8%ae%a1/" rel="category tag">
             统计
            </a>
            ,
            <a href="http://blog.cos.name/taoshi/category/%e7%bb%9f%e8%ae%a1%e5%ba%94%e7%94%a8/" rel="category tag">
             统计应用
            </a>
            ,
            <a href="http://blog.cos.name/taoshi/category/%e9%97%b2%e6%89%af/" rel="category tag">
             闲扯
            </a>
           </span>
           <span class="comments">
            <span class="icon">
            </span>
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             2 Responses »
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         <div class="date">
          <span class="month">
           12月
          </span>
          <span class="day">
           28
          </span>
          <span class="year">
           2011
          </span>
         </div>
        </header>
        <!-- /.title-container -->
        <span class="post-format-icon">
        </span>
        <span class="updated" title="2011-12-28T14:24:48+00:00">
        </span>
        <div class="entry-container fix">
         <div class="entry entry-content fix">
          <p>
           <strong>
            <a href="http://news.ifeng.com/mainland/special/kongqizhiliang/content-3/detail_2012_01/21/12115990_0.shtml" target="_blank">
             更新
            </a>
           </strong>
           （1.21.2012, 农历新年新又有气象）：北京开始公布PM2.5研究性监测小时浓度数据。“滚动播报的20日8时至21日7时的“车公庄站”PM2.5研究性数据，从数据上看浓度
           <strong>
            非常低
           </strong>
           ，最低小时浓度为每立方米0.003
           <strong>
            毫克
           </strong>
           ，最高小时浓度为每立方米0.062
           <strong>
            毫克
           </strong>
           ，出现在20日13时” 。单位是毫克，不是微克，数字当然小了。
          </p>
          <p>
           <strong>
            <a href="http://news.sina.com.cn/c/2012-01-01/022723733101.shtml" target="_blank">
             更新
            </a>
           </strong>
           （1.1.2012, 新年新气象）：“环保部常务会议审议并原则通过了《环境空气质量标准》，PM2.5(大气中直径小于或等于2.5微米的颗粒物)平均浓度限值和臭氧8小时平均浓度限值将纳入空气质量标准。[…] 根据《标准》，在全国普遍硬性施行PM2.5监测的时间是2016年。”
          </p>
          <p>
           我对“
           <a href="http://baike.baidu.com/view/697089.htm" target="_blank">
            民科
           </a>
           ”这词的理解:“对某个问题有兴趣，但没充足研究资源的科研爱好者”。我现在就对吵的沸沸扬扬的pm2.5研究很有兴趣，但是我没有足够的背景知识，足够的数据, 和足够的时间和资金来开展研究。没心情再去读个Ph.D, 那就只好民科一下我所知道的了。
          </p>
          <p style="text-align: center;">
           <img src="http://i3.sinaimg.cn/dy/cr/2011/1031/1429585082.jpg"/>
          </p>
          <p>
           今年秋冬交界的时节，大众和媒体开始
           <a href="http://news.sina.com.cn/z/air2011/index.shtml" target="_blank">
            关心pm2.5
           </a>
           的起因好像是大陆华北地区大雾带来的交通和健康方面的忧虑。让大家最不解的是官方公布的空气质量信息（“轻度污染”）和窗外的景象太不相关。很快大家明白原来官方公布的空气质量信息标准中没有考虑到pm2.5，空气中直径小于2.5微米的飘尘。用搞统计的话说，官方公布的空气质量信息是个有骗估计（虽然这个说法有点牵强）。既然知道了问题根源，那我们就解决问题呗。怎么办呢？
           <a href="http://news.sina.com.cn/green/news/roll/2011-11-17/115023481427.shtml" target="_blank">
            制定标准先
           </a>
           ！而且又发现原来pm2.5的观测数据还没有，那就
           <a href="http://city.sina.com.cn/focus/t/2011-12-28/185526153.html" target="_blank">
            开始建立观测点
           </a>
           吧。
          </p>
          <p>
           看：统计的用途就要出场啦！既然建立一些观测站的目的是监测某个区域的pm2.5在空间和时间分布，在那里观测就有些学问了。如果减小在全区域的估计误差，空间统计和试验设计就派上用场了。再加上资金和可选点的限制，一个实在应用统计问题就出炉了。这方面俺还不太民科 :)。
          </p>
          <p>
           在地面上还没建成观测站时，还有神马办法呢？那只好靠天吃饭了。抬头看，卫星图片飞过来。遥感数据或许有用。虽然我民科，可我认识的两位专家（
           <a href="http://www-misr.jpl.nasa.gov/aboutUs/scienceTeam/index.cfm?FuseAction=ShowPerson&amp;pplID=321" target="_blank">
            Ralph Kahn
           </a>
           and
           <a href="http://www-misr.jpl.nasa.gov/aboutUs/scienceTeam/index.cfm?FuseAction=ShowPerson&amp;pplID=328" target="_blank">
            Yang Liu
           </a>
           ）还真的在这让面发表过文章 （Estimating Fine Particulate Matter Component Concentrations and Size Distributions Using Satellite- Retrieved Fractional Aerosol Optical Depth,
           <a href="http://capita.wustl.edu/capita/capitaReports/091013_AQRS/AQRS/Particulates/Correlations/Liu%20and%20Kahn_2007a_AerosolComponentConcentrations_JAWMA.pdf" target="_blank">
            part1
           </a>
           and
           <a href="http://capita.wustl.edu/capita/capitareports/091013_AQRS/AQRS/Particulates/Transport_Characterization/Liu%20and%20Kahn_2007b_AerosolCOmponentConcentrations_JAWMA.pdf" target="_blank">
            part2
           </a>
           )。通过卫星观测到的尘粒光学特征来估计地面附近的pm2.5情况。了解回归分析的应该对下面的图不陌生。
          </p>
          <p>
           <a href="http://i.imgur.com/9jbnk.jpg">
            <img src="http://i.imgur.com/9jbnk.jpg"/>
           </a>
          </p>
          <p>
           再跑题一下，看了
           <a href="http://weibo.com/n/%E5%8C%97%E5%A4%A7%E5%8C%BB%E5%AD%A6%E9%83%A8%E6%BD%98%E5%B0%8F%E5%B7%9D" target="_blank">
            北大医学部潘小川
           </a>
           的关于
           <a href="http://talk.weibo.com/ft/201112213163" target="_blank">
            pm2.5的健康危害的微访谈
           </a>
           ，挺好挺有用！中间提到“最好减少汽车出行，多做公交，多步行。可以减少排放。” 最近刚好看过一篇研究韩国首尔地铁中尘粒分布的文章［
           <a href="http://www.ncbi.nlm.nih.gov/pubmed/18036738" target="_blank">
            Spatial distribution of particulate matter (PM10 and PM2.5) in Seoul Metropolitan Subway stations
           </a>
           ］，文中提到乘车区域和车内的pm10和pm2.5水平相对比售票区，休息区和员工工作区的水平高（看看看，统计又来了）。尽管这是韩国的地铁数据，但也让人对一般公交内空气质量有所担心。为了减排，我先多吸收一点。好像有点“我不入地狱，谁入地狱”的悲壮情节。
          </p>
          <p>
           不管怎么样，我还是停留在民科的层次为好，只做信息整合（meta-analysis), 没资源做实际研究，所以别全信我整合的！民科我是负不起问责制地 :)， 因为我没有收集和分析原始数据，只是以鹅传鹅而已。
          </p>
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            Teaching with David Freedman
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           11月
          </span>
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           18
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          <span class="year">
           2011
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          <p>
           In the last semester at Berkeley, I worked as a Graduate Student Instructor (GSI, the name for teaching assistant in the UC system). The course was an undergraduate intro statistics class and the instructor is David Freedman. When I got his email asking me to be his TA, I had no choice except agreeing. In my advisor’s words, “When he asks someone to be his TA, treat it as an honor and just do it.”
          </p>
          <p>
           When David passed away in October 2008 at age 70, the
           <a href="http://berkeley.edu/news/media/releases/2008/10/20_freedman.shtml" target="_blank">
            press release from Berkeley
           </a>
           noted:
          </p>
          <p>
           <em>
            “Freedman’s transition from being a mathematical statistician to a creative practitioner of applied statistics occurred in part, by his own account, in response to the challenges of undergraduate teaching on the UC Berkeley campus,” Collier said. “His students were bored with statistics courses and with the abstracted examples that were standard fare in textbooks,” leading Freedman to dig up practical examples in many applied areas. [……]
           </em>
          </p>
          <p>
           <em>
            He wrote six textbooks, including the highly regarded undergraduate text, “
            <a href="http://www.amazon.com/Statistics-4th-David-Freedman/dp/0393929728/" target="_blank">
             Statistics
            </a>
            ,” with co-authors Robert Pisani and Roger Purves, now in its fourth edition. The book is “a widely used undergraduate textbook, crystal clear, a delight to read and to teach from, broad, deep and meticulously accurate in every detail,” Stark said. “It transformed the way many people taught statistics from a formula-driven, plug-in-the-numbers approach to a focus on
            <strong>
             critical thinking
            </strong>
            .”
            <img src="http://ecx.images-amazon.com/images/I/41FTnmlZVbL._SS500_.jpg"/>
           </em>
          </p>
          <p>
           At first, it seemed strange that he insisted me to attend all his lectures. Being a TA, I have never been required to attend the lecture. Sometimes, you just got to do what you were told to do :). I went to his lectures every time and I had to admit at the end that I learned things I have never thought about. He had constantly drawn straightforward but misled conclusions from data and challenged the students to criticize his arguments. You almost never knew he is telling you what to believe or what not to believe before he turned the face of the card. However, he always gave a convincing concluding remark to summarize the whole discussion. It is fascinating to see critical thinking in full display.
          </p>
          <p>
           I clearly remember two pieces of advises he gave me during that semester. When I asked him for advise on job interviews before I went on to my first one, he told me, “Look, I’m a
           <strong>
            famous
           </strong>
           statistician. However, I know nothing about what you are doing. When you gave the talk, you are the most knowledgeable person on these topics in the room. Just tell your story and be yourself. If they like you, they will hire you. If they don’t, there is nothing you can do and nothing to worry about.” His words gave me confidence to behave just like myself without worrying.
          </p>
          <p>
           He offered me another piece of advisor after we finished grading the finals in his house. As I was walking towards the door, I somehow felt emotional and wanted to say a few words to him. I wanted to thank him for his support and guidance. He looked calmly and told me, “Just do what you feel right to do, and don’t look around.” It was the last time I talked to him. Very often, I feel I owe him so much. I cannot pay anything back to him and the best I can do is passing what I learned from him to others. This is the main reason why I’m writing these blog posts — it just feels right.
          </p>
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            Statistical consulting with David Freedman
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           12
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           2011
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          <p>
           One year after taking applied statistics course from David, I signed up for his second “class”,
           <a href="http://www.stat.berkeley.edu/43" target="_blank">
            statistical consulting
           </a>
           . It was a rare opportunity  to learn from someone with so much experience in consulting.
          </p>
          <div class="wp-caption aligncenter" style="width: 410px">
           <img src="http://owpdb.mfo.de/photoNormal?id=9385"/>
           <p class="wp-caption-text">
            David A. Freedman (copyright: George M. Bergman)
           </p>
          </div>
          <p>
           According to
           <a href="http://en.wikipedia.org/wiki/David_A._Freedman_(statistician)" target="_blank">
            Wikipedia
           </a>
           , “Freedman was a consulting or testifying expert on statistics in disputes involving
           <a href="http://en.wikipedia.org/wiki/Employment_discrimination" title="Employment discrimination">
            employment discrimination
           </a>
           , fair loan practices,
           <a href="http://en.wikipedia.org/wiki/Voting_rights" title="Voting rights">
            voting rights
           </a>
           , duplicate signatures on petitions, railroad taxation,
           <a href="http://en.wikipedia.org/w/index.php?title=Ecological_inference&amp;action=edit&amp;redlink=1" title="Ecological inference (page does not exist)">
            ecological inference
           </a>
           , flight patterns of
           <a href="http://en.wikipedia.org/wiki/Golf_ball" title="Golf ball">
            golf balls
           </a>
           ,
           <a href="http://en.wikipedia.org/w/index.php?title=Price_scanner&amp;action=edit&amp;redlink=1" title="Price scanner (page does not exist)">
            price scanner
           </a>
           errors,
           <a href="http://en.wikipedia.org/wiki/Bovine_Spongiform_Encephalopathy" title="Bovine Spongiform Encephalopathy">
            Bovine Spongiform Encephalopathy
           </a>
           (Mad Cow disease), and sampling. He consulted for the
           <a href="http://en.wikipedia.org/wiki/Bank_of_Canada" title="Bank of Canada">
            Bank of Canada
           </a>
           , the
           <a href="http://en.wikipedia.org/w/index.php?title=Carnegie_Commission&amp;action=edit&amp;redlink=1" title="Carnegie Commission (page does not exist)">
            Carnegie Commission
           </a>
           , the
           <a href="http://en.wikipedia.org/wiki/San_Francisco" title="San Francisco">
            City of San Francisco
           </a>
           , the
           <a href="http://en.wikipedia.org/wiki/County_of_Los_Angeles" title="County of Los Angeles">
            County of Los Angeles
           </a>
           , and the
           <a href="http://en.wikipedia.org/wiki/Federal_Reserve" title="Federal Reserve">
            Federal Reserve
           </a>
           , as well as the U.S. departments of energy, treasury, justice, and commerce.”
          </p>
          <p>
           Guess that was the first task we were assigned for the class? Wondering around the campus and posting posters in every building. I know it sounds like old school, but we needed clients :). When there is no problem, there is no need for data or analysis.
          </p>
          <p>
           I remember the first project my partner and I run into was money involved. We were excited to help predicting which alumni has a high tendency to donate back to Berkeley. It was funny that zip code was such a powerful predictor and it is almost universally powerful in most of social studies (as David told us). “location, location, and location!”
          </p>
          <p>
           The most practical (and painful) lesson we learned from David in consulting is that he insisted us not to give any suggestion in the first meeting. See, “we are eager to help, but … ” We should not give answers or suggestions to any problem without understanding its background, even if what the client asked is just how to calculate a p-value using a t-test. It is funny how our profession has squeezed into this corner. In many situations, as soon as one gets a p-value &lt; 0.05, one hypothesis is rejected and (statistically) significant finding is claimed. Ironically, statisticians who created the p-values are the ones who are acting like policemen holding the last stands now. Critically investigating the problem and the data together may push us a few steps closer to “be useful”. Otherwise, we probably act no better than a software package (like SAS or R).
          </p>
          <p>
           In the meanwhile, we also learned to be creative when working with clients who have already collected their data according a “flawed” design. We need help them. In David’s words, “Treat them as your patients. If your patient is bleeding, it does not help to blame them on how they get into an accident. Stop the bleeding and save him/her first, then educate.”. In many cases, how to help them with an imperfect design or data involves developing and researching for new statistical methods. It all goes back to why statistics exists as a field in the first place: help people answer questions with data.
          </p>
          <p>
           [To be continued ……]
          </p>
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          <p>
           <a href="http://blog.cos.name/taoshi/files/2011/11/book.jpg">
            <img src="http://blog.cos.name/taoshi/files/2011/11/book.jpg?w=175"/>
           </a>
           Just suit up for the first time since the job interview season of Winter 2005. As one of those who got promoted this year, I attended the
           <a href="http://library.osu.edu/blogs/librarynews/2011/10/27/faculty-recognition-program-november-7/" target="_blank">
            Faculty Recognition Program
           </a>
           today and got a chance to look closely at the book, “
           <a href="http://www.amazon.com/Statistical-Models-Practice-David-Freedman/dp/0521743850" target="_blank">
            Statistical Models, Theory and Practice
           </a>
           ” by
           <a href="http://www.stat.berkeley.edu/~freedman/" target="_blank">
            David A. Freedman
           </a>
           , that I “donated” (the university is paying for it) to the library. I’m honored to be able to pay my tribute to him, who open my world of useful statistics.
          </p>
          <p>
           The firs time I met David was when I took his applied statistic class at Berkeley. At that time, I passed my Ph.D. qualify exam, started with some research projects and felt like I knew Statistics. In his class, I learned quickly that I knew NOTHING about Statistics except some abstract mathematical symbols.
          </p>
          <p>
           The first meeting of the class was the most wield period I have sit thought as a student. Several days before the first class, every student got his email in which a list of three papers were assigned for reading. We were good students so we all read the paper carefully, I assume at least. Then here came the first meeting of the class. David came in and explained several things about the course structures before we went to the more serious business, paper discussion.
          </p>
          <p>
           The discussion started like this. He calmly sit in a chair and asked “questions?”. Silence, …, silence, …, and longer silence. We had not expected that we were supposed to “initiate” the discussion instead of “participating”. Then he asked gain, “questions on the paper?”. We just sit there for more than 10 minutes, flipping through papers  and trying to find a meaning question to ask. Seems like we would sit there forever if we did not come up with any questions. It felt like the longest class I have ever taken.
          </p>
          <p>
           The format of the class kept the same way for the whole semester. Students first read the assigned papers and come up with questions about anything and everything about the paper. David entertained every question. The questions ranges from “why have the authors picked this model?” to “why have the authors NOT picked that model?”, from “Does the model fit the data well?” Do the data support the model at all?”, from “Does the evidence support the conclusion?” to “Is the evidence related to the conclusion?”. The interesting part is that about one third of the papers we discussed were written by him. We have to (at least try to) criticize the paper and he enjoys doing do as well. Sometime he would defend the paper for while and then add his own critics.
          </p>
          <p>
           Trying to recall what I have learned from the class, the most striking thing is that I cannot remember what models or methods we went over (there are a lot of them). I have to admit that I have a really bad memory. At the same time, I do remember one thing: Check the model assumptions and check them again and again.
          </p>
          <p>
           In an age of having so much computation power and advanced software packages to run almost any analysis in a short period time, our ability in drawing proper conclusion from data through mathematical inference is not necessarily improved unless we carefully check if we are using the right tools to answer the right questions. Thinking critically about each step of any analysis is the key to a proper conclusion. This is the first lesson I learned from David.
          </p>
          <p>
           [To be continued ……]
          </p>
          <hr/>
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           <a class="entry-title" href="http://blog.cos.name/taoshi/2011/11/02/application/" rel="bookmark" title="从另个角度看出国留学申请">
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           11月
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           02
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           2011
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          <p>
           co-author: 陈甜
          </p>
          <p>
           学统计和做统计的大概都认为统计也算是一门学问（“大概”对统计很重要）。很多人在学习的过程中或早或晚都会听到过这样的说法：“统计中重要的不只是我们看到的（数据），更重要的是我们没有看到的（总体，抽样偏差，模型偏差，数据处理错误, 。。。）。”对于留学出国申请，很多同学有的一个迷思就是，我知道要有GPA，GRE,和TOFEL成绩，我也知道 Personal Statement 和推荐信很重要，可是录取到底是一个什么样的过程，而教授们重视的又是哪些呢？下面就让我们来分析一下，想一下：
          </p>
          <p>
           大丈夫有所为有所不为。首先最重要的问题就是为什么要出国留学读博士。既然要把这么多的时间和精力投入一件事情，总得给自己一个理由吧。很多人在 Personal Statement 里这么写，“我对统计研究感兴趣”，“我想到国外感受一下”，“别人读了，我为神马不读”，“我妈妈让我读”或者是“我要追的人出国了”等等。可是如果仔细推敲一下，你是不是能用这些说服你自己呢？我们这里谈的是你接下来5,6年的时光甚至是你一辈子的职业走向，而不是中午吃红烧肉还是番茄炒蛋的问题。就说“我想到国外感受一下”这个理由吧，你想要感受可以出国旅游，想读书可以申请短时间的交换，就为了感受一下花5,6年的时间，你自己是不是信服呢？在申请开始之前，请放下手里的事情，好好审视一下自己的内心，给自己一个想要出国读博的理由。因为博士学习是一个很长很辛苦的过程。你会远离自己的亲人和朋友，要自己处理很多状况，而在你为了学习和生活两头忙的时候，很多你的朋友可能已经开始赚钱养家，结婚生子，走上和你全然不同的人生道路。所以最好你能找到一个足以支持你走到最后毕业和更远的读博理由，而不是为了入学邀请和奖学金。理由不足以说服自己的，可以放松一下去看小说了。
          </p>
          <p>
           好。现在你决定要申请了。那么我们就先翻墙去看看围城的另外一面（你可能不知道的），然后再回来看我们应该怎么做。
          </p>
          <p>
           <span class="more-link fix">
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           <a class="entry-title" href="http://blog.cos.name/taoshi/2011/10/07/science_twitter_work/" rel="bookmark" title="《科学》：Twitter反映人们工作压力巨大 (真的吗？)">
            《科学》：Twitter反映人们工作压力巨大 (真的吗？)
           </a>
          </h2>
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             媒体报道
            </a>
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            <a href="http://blog.cos.name/taoshi/category/%e6%95%b0%e6%8d%ae%e5%88%86%e6%9e%90/" rel="category tag">
             数据分析
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             科学
            </a>
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            <a href="http://blog.cos.name/taoshi/category/%e7%bb%9f%e8%ae%a1%e5%ba%94%e7%94%a8/" rel="category tag">
             统计应用
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         <div class="date">
          <span class="month">
           10月
          </span>
          <span class="day">
           07
          </span>
          <span class="year">
           2011
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          <p>
           今天偶然在
           <a href="http://news.sina.com.cn/m/2011-09-30/113923244615.shtml" target="_blank">
            新浪新闻
           </a>
           上读到这个标题，吓了一跳（当然“真的吗？”是我后加的）。文中提到:
          </p>
          <p>
           “
           <em>
            北京时间9月30日上午消息，美国《科学》杂志撰文称，从Twitter上发布的微博来看，全世界人们普遍承受着巨大的工作压力。
           </em>
          </p>
          <p>
           <em>
            康奈尔大学的研究者对84个国家240万用户最近两年发表的微博进行了研究，并指出每一天人们都在快乐中醒来，可只要一开始工作，好心情就会荡然无存。
           </em>
           ”
          </p>
          <p>
           前几天我
           <a href="http://taoshistat.wordpress.com/2011/10/02/learning-from-twitter/" target="_blank">
            刚闲扯
           </a>
           了关于
           <a href="http://www.redlog.net/" target="_blank">
            Scott A. Golder
           </a>
           和
           <a href="http://sdl.soc.cornell.edu/mwm/" target="_blank">
            Michael W. Macy
           </a>
           在《科学》上发表的
           <a href="http://www.sciencemag.org/content/333/6051/1878.abstract" target="_blank">
            用twitter数据分析关于人的情绪与一天时间变化及其他的关系
           </a>
           （以及有趣的实际演示网页
           <a href="http://timeu.se/" target="_blank">
            http://timeu.se/
           </a>
           ）。虽然我的脑筋很差，但是还依稀记得这篇文章的一些细节。下面是原文中关于心情和工作的部分（1880页）和数据图形（PA 是 positive affect, 正面影响；NA是 negative affect, 负面影响），大家来看看翻译和原文有无出入？
          </p>
          <p>
           “Although the shape of the rhythm was consistent across days, PA levels were generally higher on Saturday and Sunday (M = 0.058) than at any time during the weekdays (M = 0.054, P &lt; 0.001), which points
           <strong>
            to possible effects of work-related stress,
           </strong>
           less sleep, and earlier wake time. PA decreased midmorning (at the start of the work day) and increased in the evening (at the end of the work day).
           <strong>
            However
           </strong>
           ,
           <strong>
            the fact that the shape of the affective cycle was similar on weekends and weekdays points to sleep and the biological clock as important determinants of affect, regardless of variations in environmental stress.
           </strong>
           ”
          </p>
          <p style="text-align: center;">
           <a href="http://i.imgur.com/Xi006.jpg">
            <img src="http://i.imgur.com/Xi006.jpg"/>
           </a>
          </p>
          <p>
           原文强调的是人的生物时钟的影响，而不是工作与否。在信息爆炸的今天，断章取义太容易也太误导了吧。
          </p>
          <p>
           作为新闻媒体，你们有木有责任尽量保证文章的准确性和全面性呢？有木有？简单的翻译也要有职业精神啊。请不要把手机山寨成微波炉！
          </p>
          <p>
           作为读者，我们是不是要去调查每一个故事背后的数据和分析的准确性呢？这当然不可能了，但我们至少可以以科学的态度去审视和相信（或怀疑）我们看到的故事。
          </p>
          <p>
           转载过超过两次的报道，不管你信不信，反正我是不信！
          </p>
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           <a class="entry-title" href="http://blog.cos.name/taoshi/2011/09/21/fcwr/" rel="bookmark" title="非诚勿扰">
            非诚勿扰
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            </a>
            ,
            <a href="http://blog.cos.name/taoshi/category/%e6%95%b0%e6%8d%ae%e6%8c%96%e6%8e%98/" rel="category tag">
             数据挖掘
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             闲扯
            </a>
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          <span class="month">
           9月
          </span>
          <span class="day">
           21
          </span>
          <span class="year">
           2011
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        <span class="updated" title="2011-09-21T13:48:00+00:00">
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          <p>
           儿子最近喜欢上了看
           <a href="http://fcwr.jstv.com/" target="_blank">
            这个节目
           </a>
           （特别爱听灯灭时的声音）。反正是学中文，看就看呗。
          </p>
          <p>
           有趣的是
           <a href="http://www.youtube.com/watch?v=qFtnab5PKOE" target="_blank">
            最近一期
           </a>
           <em>
           </em>
           来了位
           <a href="http://weibo.com/firmwolf" target="_blank">
            杨健
           </a>
           同学，做了个关于节目中男同学牵手成功率的数据分析（
           <a href="http://www.youtube.com/watch?v=qFtnab5PKOE" target="_blank">
            <em>
             8
            </em>
            ：
            <em>
             15
            </em>
            －
            <em>
             10
            </em>
            ：
           </a>
           <em>
            <a href="http://www.youtube.com/watch?v=qFtnab5PKOE" target="_blank">
             55
            </a>
           </em>
           或
           <em>
            <a href="http://ent.joy.cn/video/3162364.htm" target="_blank">
             34
            </a>
           </em>
           <a href="http://ent.joy.cn/video/3162364.htm" target="_blank">
            ：
            <em>
             00
            </em>
            －
            <em>
             37
            </em>
            ：
            <em>
             00
            </em>
           </a>
           ）, 挺有意思。他根据108－157的50期节目的数据（2011.3.5-8.21，去掉了2个澳洲专场和1个520腾讯专场，这3场不具备数据抽样的典型性），总结了各类背景共236名男同学的牵手成功率，比如：
           <em>
            30
           </em>
           －
           <em>
            34
           </em>
           岁
           <em>
           </em>
           （
           <em>
            24.19
           </em>
           ％），山东人（
           <em>
            23.08
           </em>
           ％）
           <em>
           </em>
           ，
           <em>
            IT
           </em>
           男
           <em>
           </em>
           （
           <em>
            5.88
           </em>
           ％
           <em>
           </em>
           ）。对于作为身兼以上三条的男同学（
           <a href="http://blog.sina.com.cn/u/1160133813" target="_blank">
            杨健
           </a>
           同学也是其中之一），节目播出前成功率为
           <em>
            0
           </em>
           ％。很不幸，播出后还是
           <em>
            0
           </em>
           ％。但从另一方面也证明了两件事：一，对历史数据的分析还是有一定预测价值的；二，
           <em>
           </em>
           概率为 0 的事件还是很难发生的。（当职业病发作时，我还会加一条： 这数据不象抽样，更象普查。）
          </p>
          <p>
           看来数据挖掘潜力还是很大吗！好。那么我们来回头展开想一下。根据
           <em>
            150
           </em>
           多期的节目录像，数据挖掘可以做什么？或许可以搭建一些黑盒子（也就是算法）。我们可以把女同学们（从男同学上场到第一次有机会灭灯之间）的表情扔进一个盒子，盒子很短时间内给出这次几盏灯会灭的预测，当然更好的盒子或许还可以预测到底哪几盏灯会灭。或者我们可以把孟非，黄菡，乐嘉的评论扔进另一个盒子，现场预测最后成功率，等等等等。。。
          </p>
          <p>
           我们首先会问了：
           <em>
            “
           </em>
           就算我们能搭建这些盒子，它们有用吗？
           <em>
            ”
           </em>
           这当然看人怎么用它们了。有些可能有科学价值（明白第一个盒子内的结构对心理学还是有用），有些可能有商业价值（第二个例子，我们摆个盒子在现场做为第四位老师来作预测，收视率肯定会提高），当然还有很多盒子可能没有任何作用，只是好玩。
          </p>
          <p>
           在确定了那些盒子可能有用后，下个问题才是关键：
           <em>
            “
           </em>
           我们能搭建这些盒子吗（
           <em>
           </em>
           当然要求这样的盒子有一定的精确度）？
           <em>
            ”
           </em>
           这也是对数据挖掘研究的挑战，搭建简单的盒子或许不难，但要做一个好的盒子需要很多方面的专业人才的合作：图形处理，语音识别，心理学，经济学，统计学，人工智能，算法，优化，。。。
           <em>
           </em>
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           有兴趣搭这些盒子吗？非诚勿扰！
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